NDVI changes in the Arctic: Functional significance in the moist acidic tundra of Northern Alaska

The Normalized Difference Vegetation Index (NDVI), derived from reflected visible and infrared radiation, has been critical to understanding change across the Arctic, but relatively few ground truthing efforts have directly linked NDVI to structural and functional properties of Arctic tundra ecosystems. To improve the interpretation of changing NDVI within moist acidic tundra (MAT), a common Arctic ecosystem, we coupled measurements of NDVI, vegetation structure, and CO2 flux in seventy MAT plots, chosen to represent the full range of typical MAT vegetation conditions, over two growing seasons. Light-saturated photosynthesis, ecosystem respiration, and net ecosystem CO2 exchange were well predicted by NDVI, but not by vertically-projected leaf area, our nondestructive proxy for leaf area index (LAI). Further, our data indicate that NDVI in this ecosystem is driven primarily by the biochemical properties of the canopy leaves of the dominant plant functional types, rather than purely the amount of leaf area; NDVI was more strongly correlated with top cover and repeated cover of deciduous shrubs than other plant functional types, a finding supported by our data from separate “monotypic” plots. In these pure stands of a plant functional type, deciduous shrubs exhibited higher NDVI than any other plant functional type. Likewise, leaves from the two most common deciduous shrubs, Betula nana and Salix pulchra, exhibited higher leaf-level NDVI than those from the codominant graminoid, Eriophorum vaginatum. Our findings suggest that recent increases in NDVI in MAT in the North American Arctic are largely driven by expanding deciduous shrub canopies, with substantial implications for MAT ecosystem function, especially net carbon uptake.


Introduction
Because large swaths of the Arctic are remote, satellites equipped with sensors that quantify reflected visible and infrared radiation at large scales are critical to understanding the effects of high-latitude climate change [1][2][3][4][5]. In particular, NDVI has frequently been used to estimate vegetation biomass, leaf area, or species composition [6][7][8][9], often with the goal of predicting ecosystem functional attributes, such as photosynthetic and respiratory fluxes of CO 2 [9][10][11][12]. However, accurate interpretation of the NDVI signal in the Arctic remains limited by relatively few ground truthing efforts [7,13], hindering our ability to understand the causes and functional consequences of contemporary NDVI trends [14]. NDVI has increased ("greening") across large portions of the Arctic since the 1980's [15][16][17][18][19]. This trend is spatially heterogeneous, and has in some areas slowed, with other areas showing declining NDVI ("browning") [20,21]. However, the higher NDVI of deciduous shrub tundra relative to other tundra types [22,23] has led many researchers to suggest that increasing NDVI indicates a widespread increase of deciduous shrubs in the Arctic [2,3]. High-resolution photographs and field validation have confirmed the linkage between increasing Landsat NDVI and expanding shrub cover in some parts of the Arctic [7,13,18,[23][24][25][26]. However, questions remain regarding how the Arctic NDVI signal, beyond indicating shifts in vegetation community composition, relates to ecosystem structure and function.
In several cases the relationship between NDVI and LAI has been used to extend the utility of NDVI to metrics of ecosystem function, including estimates of gross primary productivity (GPP) and net ecosystem CO 2 exchange (NEE) [10,11]. Often in such efforts, ground truth plots are sampled across a range of vegetation communities or experimental treatments, and in this context NDVI has frequently demonstrated a strong relationship with LAI or total biomass and, subsequently, CO 2 uptake [6,[9][10][11][12]. However, when sampled within the more productive vegetation types that are critical to the Arctic carbon (C) cycle it remains to be demonstrated whether spatial or temporal variation in NDVI indicates changes in leaf area and/or deciduous shrub abundance. For example, moist acidic tussock tundra (MAT) is a widespread, highly productive Arctic ecosystem which holds globally important stocks of soil C [27]. Within MAT, peak-summer LAI values often approach or exceed an LAI of 1.0 [13,28,29]; above this threshold, the observed NDVI-LAI relationship may be confounded, as higher LAI values will typically coincide with greater canopy layering.
The goal of this project was to use in situ measurements of vegetation structure and ecosystem function to improve interpretation of changing NDVI within MAT. We aimed to answer two key questions. First, does increasing NDVI in MAT indicate an increase in leaf area, deciduous shrub abundance, or both? And second, what are the implications of observed variation in NDVI for NEE, GPP, and ecosystem respiration (R eco )? To answer these questions, we coupled measurements of NDVI in 70 MAT plots (0.49 m 2 ) with NDVI measurements from 199 homogeneous plots (0.03 m 2 ) of MAT cover types and 89 leaves of the three dominant species. We combined this with measurements of ecosystem CO 2 exchange in the MAT plots over two growing seasons. We discuss our findings in the context of models commonly used in predicting pan-Arctic NEE and take the additional step of parameterizing and evaluating two of these models with our intra-MAT dataset.

Study site
This study was conducted primarily during the summers of 2010 and 2011 at Toolik Lake Field Station (68˚38'N, 149˚36'W, elevation 720 m) and within the Imnavait Creek watershed 10 km north of Toolik Lake in northern Alaska (S1 Fig). Permits for the work were provided by Toolik Lake Field Station and the Bureau of Land Management. The broader study area is in the northern foothills of the Brooks Range and is characterized by gentle, rolling topography. Average annual air temperature is about -7˚C, mean annual precipitation is 322 mm, and the area is snow-covered approximately 8 months of the year with a mean growing season length of 125 days [30].
MAT in this region is dominated by the tussock forming sedge, Eriophorum vaginatum, deciduous dwarf shrubs (primarily Betula nana (dwarf birch) and Salix pulchra (diamond-leaf willow)), and dwarf evergreen shrubs (mainly Vaccinium vitis-idaea (lingonberry) and Ledum palustre (Labrador tea)). The soil surface is composed primarily of moss and litter.

Plot selection
To provide inference across a broad range of potential MAT vegetation conditions, 70 sample plots (70 x 70 cm) were established in MAT areas that demonstrated a wide range of deciduous shrub and graminoid cover. Fifty-three plots were sampled near Toolik Lake and seventeen near Imnavait Creek.

Species cover and leaf area
We estimated species composition in our plots using the point-frame technique with 100 evenly spaced points [31] in a 0.49 m 2 frame. The pin, a 3 mm diameter x 1 m long rod, was lowered at each intersection, and each contact with plant or litter was recorded. We itemized 13 species of vascular plants as well as moss, lichen, litter, and standing dead material (leaves and stems), for a total of 17 categories. The 'top cover' of each species was calculated as the proportion of first pin hits whereas 'repeated cover' was calculated as the proportion of all pin hits [32]. Vertically projected leaf area (LA VP ) of each vascular plant species was calculated as the sum of all pin hits attributed to that species' leaves divided by 100 (the maximum number of hits in a single layer); this served as a non-destructive estimator of plot-level LAI. Pin intercept methods for LAI estimation have been shown to be highly correlated with harvest-based LAI, typically explaining 80-90% of the variation [33,34]. LA VP would equal LAI if all leaves were arranged perpendicular to the pin [32]. However, accounting for variation in leaf orientation is challenging and was not attempted here. Plot-level LA VP was calculated in the same manner as species-level but with leaf pin hits summed across all vascular plant species. We also calculated LA VP with all moss pin hits included and discuss the implications of this using this version. Top and repeated cover and LA VP were later grouped into eight cover types (CTs): deciduous shrub, evergreen shrub, graminoid, forb, lichen, litter, moss, and standing dead material. The live CTs are henceforth referred to as plant functional types (PFTs) and represent groupings commonly used in Alaskan vegetation studies [26,35]. Species groupings can be viewed in Fig 2.

Spectral reflectance measurements
A dual channel portable spectroradiometer (Unispec-DC, PP Systems International, Inc., Amesbury, MA) was used to measure plot-level reflectance, usually at the same time as the flux measurements and in all cases within three hours of solar noon and within two days of measuring plot CO 2 exchange. The upward looking foreoptic of the spectroradiometer, covered by a diffuser, measures hemispheric irradiance of the sky. The downward looking foreoptic measures conic radiance of the vegetation and has a field of view that is approximately 20˚; this was placed h = 199 cm above plot center such that the radius of the field of view was r = 35 cm (given that tan 10˚= r/h and tan 10˚= 0.1763). The two channels of the spectroradiometer with their attached foreoptics were calibrated according to manufacturer recommendations using a small, highly reflective Spectralon disk (Labsphere, Inc., North Sutton, NH). Plot radiance and sky irradiance were simultaneously logged by the Unispec-DC. NDVI was calculated using average reflectance in the red (620 nm to 670 nm), and near infrared (841 nm to 876 nm) spectral regions, which correspond to MODIS bands 1, and 2, respectively, using the formula: where R nir and R red are the average reflectance in the near infrared and red bands, respectively. We measured reflectance on our plots at approximately two-week intervals, beginning on June 24 and ending on August 14.
To better understand the contribution of different CTs to the spectral signature of MAT, we measured reflectance of the dominant CTs two ways: as monotypic stands of important CTs and as individual leaves of B. nana, S. pulchra, and E. vaginatum. For 199 naturally occurring monotypic stands at least 20 cm in diameter (20 graminoid, 94 deciduous shrub, 31 evergreen shrub, 21 moss, 4 standing dead material in the canopy, and 29 litter) we measured reflectance using the same technique as in the flux plots (but with the downward looking foreoptic fixed at 57 cm height for a 20 cm diameter field of view). Selecting only patches of pure composition resulted in uneven sample sizes, but the two most abundant and ecologically important PFTs (deciduous shrubs and graminoids) were well represented. For individual leaves of B. nana, S. pulchra, and E. vaginatum (n = 31, 31, 27, respectively) we measured reflectance spectra in the field with a Unispec-DC on June 28 through July 3, 2013. A device was constructed to hold the leaves exactly 1.7 cm below the downward looking optic so the diameter of the field of view was 1 cm (S1 Fig). Leaves were collected and immediately fastened to black blocks with black plastic tape and scanned in the field. Leaves of B. nana and S. pulchra are generally more than 1 cm wide so only one leaf was used for each scan. Ten or more green E. vaginatum leaves were placed taped together such that they lay parallel with zero space between blades and were scanned at one time. Each sample was scanned in three times, rotating 90˚between scans (but always remaining perpendicular to the foreoptic), and the scans were averaged to produce a single NDVI value for each sample.

CO 2 exchange measurements
We measured plot-level CO 2 exchange (NEE, GPP, and ER) in an enclosed clear acrylic chamber (70 cm square by 40 cm high, with~.0.9 cm thick walls) using a LI-6400 portable photosynthesis system (LI-COR Environmental, Lincoln, Nebraska) attached to the side of the chamber. The chamber contained two small 2.4 W fans and a LI-190 quantum sensor (LI-COR Environmental, Lincoln, Nebraska) and was sealed against a plastic base frame fitted with a 50 cm-wide flexible vinyl skirt, which was sealed to the tundra with a heavy (~15 kg) chain. After sealing the chamber, we measured chamber CO 2 concentration every two seconds for one minute. We calculated NEE according to: where NEE is net CO 2 flux (μmol m -2 s -1 ), ρ is the average molar density of air during the measurement interval (mol m -3 ), V is the volume of the chamber (m 3 ), dC/dt is the change in CO 2 concentration over time (μmol CO 2 mol -1 s -1 ), and A is the footprint area of the chamber (m 2 ). At each plot we measured NEE under six light levels (implemented with shade cloths), ventilating the chamber between light levels by tipping it upright for 15-20 seconds until the CO 2 concentration returned to ambient. The resulting dataset included 2386 individual flux measurements. Mean ± SD R 2 values for the linear fits used to calculate NEE from the raw data were 0.92 ± 0.11. NEE was modeled as rectangular hyperbola mixed-effects model using the nlme package [36] in R, with both plot and day of year as random effects: where NEE is the net flux of CO 2 measured in the chamber (μmol m -2 s -1 ), R eco is modeled ecosystem respiration (μmol m -2 s -1 ), A max is a modeled parameter indicating maximum assimilation rate of CO 2 (μmol m -2 s -1 ), PAR is incident flux of photosynthetically active radiation measured in the chamber as (μmol photons m -2 s -1 ), and the modeled parameter k is the PAR value at half Amax (μmol photons m -2 s -1 ). Starting values for these parameters in the nlme estimation procedure were: . These values were then used to calculate GPP 600 as the sum of NEE 600 and R eco . The plot-level light compensation point (LCP), where NEE = 0 (when R eco = GPP), was determined from the x-intercept of the fitted light curve for those curves with sufficient GPP to drive NEE below zero. The initial slope of the light curve at PAR = 0, commonly referred to as quantum yield (E o ), was calculated by taking the first derivative of the light curve equation with respect to PAR and setting PAR = 0, yielding: NEE was measured between 1100 and 1600 hours at biweekly intervals throughout the 2010 and 2011 growing seasons. This frequency provided three to four midseason measurements on each of 70 plots. We report NEE values from the atmospheric perspective where negative values indicate net uptake of CO 2 by the ecosystem, and positive values indicate a net release of CO 2 to the atmosphere.

Statistical analysis
We used Pearson's correlation coefficients to examine the interrelationships in top and repeated cover between CTs as well as individual taxa. We tested for differences in LA VP among PFTs and individual species across all plots using the non-parametric Kruskal-Wallis test, followed by Dunn's post-hoc tests. To analyze the influence of the proportional cover of the CTs on plot-level NDVI we fitted linear multiple regression models with plot-level NDVI as the dependent variable and CT proportional cover as the independent variables; significant coefficients were interpreted as a CT's influence (in units of NDVI) on the plot-level NDVI signal. The idea of using the multiple regression is that the plot-level NDVI value is a linear combination of the NDVI values of the constituent taxa weighted by their proportional cover. Next, to parse the spectral contributions of individual CTs we compared reflectance in the NIR and red bands and NDVI values for the monotypic plots using Kruskal-Wallis tests, followed by Dunn's post-hoc tests. One-way ANOVAs followed by Tukey post-hoc tests were used to make the analogous spectral comparisons among the three dominant taxa. We examined the relationship between LA VP or NDVI and peak season NEE 600 , GPP 600 , and R eco using linear regression models.
Finally, we evaluated two models commonly used in the prediction of tundra productivity. The first, henceforth the "LAI~NDVI" model, has been used [10,11,37] to predict LAI from NDVI using the following relationship: We parameterized the curve using constrained nonlinear least squares estimation [38], with parameter bounds derived from Table 2 in Street [9] and the pan-Arctic parameters in Shaver (2013). Next, the Tundra GPP Model has often been used concurrently to estimate GPP using the modeled values of LAI: Where b is the Beer's law extinction coefficient, set to 0.5 as in previous studies [11,39], and the remaining parameters are as previously defined. Models were evaluated by comparing our observed values against model predicted values (e.g., LAI modeled from NDVI compared to LA VP ).
All analyses were carried out in R version 4.1.2 (R Core Team, 2021). Figures were produced using the R packages ggplot2 [40] and patchwork [41]. For multiple regression models, predictor variables were checked for collinearity, and for ANOVA and regression models all residuals were checked for normality and homogeneity of variance.

Species cover
As expected for MAT, deciduous shrubs had the highest average top cover followed by graminoids, but sample plots varied widely in the proportional contributions of deciduous shrubs and graminoids (Fig 1), satisfying the intent of the study. The mean ± standard error top cover of the deciduous shrub PFT, composed primarily of B. nana and S. pulchra, was 27.5 ± 1.6% and the mean ± standard error top cover of the graminoid PFT, composed primarily of E. vaginatum and C. bigelowii, was 21% ± 1.7%. Top cover of deciduous shrubs correlated strongly and inversely with graminoid top cover (r = -0.

Vertically projected leaf area
Vertically projected leaf area (LA VP ) varied from 0.54 to 1.51 with a mean value of 1.02 (Fig 2). The PFTs differed in plot-level LA VP (Kruskal-Wallis test, H 3 = 142, P < 0.001), due principally to differences in LA VP between forbs and the pair deciduous shrubs and graminoids. Deciduous shrubs and graminoids exhibited the highest LA VP (but did not differ significantly from each other (Fig 2B)). Individual taxa also differed in plot-level LA VP (H 12 = 447, P < 0.001), primarily due to differences between the pair E. vaginatum and B. nana, which did not differ significantly from each other, and several forb and evergreen dwarf shrub taxa.

CO 2 exchange
NDVI was a significant predictor of peak season plot-level CO 2 exchange, being negatively correlated with NEE 600 (increasing CO 2 uptake associated with increasing NDVI) and positively correlated with GPP 600 and R eco (Fig 5A). We found no evidence of a direct relationship

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between plot-level LA VP and NEE 600 , GPP 600 , or R eco unless moss was included in LA VP calculations (Fig 5B and 5C). With moss included, LA VP was a significant predictor of NEE 600 , GPP 600 , and R eco , but the fit was poor (r 2 < 0.25 for all three models).
Regression models using top cover of the five photosynthetic PFTs to predict peak season NEE 600 , GPP 600 , and R eco were significant (P < 0.001 for all three) and fit the data somewhat better, explaining 39%, 48%, and 22% of the variation, respectively (Table 3). Deciduous shrub and forb cover were negatively correlated with NEE 600 and positively correlated with GPP 600 (increasing CO 2 uptake associated with increasing shrub and forb cover), while moss also exhibited a positive effect on GPP 600 . Forb cover was also positively correlated with R eco , being the only PFT to exhibit a significant relationship with this variable.
Plot-level light curve parameters were partially predicted by PFT cover ( Table 4). The initial slope of the light curve, E 0 , was significantly predicted by cover of the five photosynthetic PFTs (F 5,62 = 10.6, r 2 = 0.46, P < 0.001), with deciduous shrubs and forbs driving steeper initial , solved for NDVI. Black line and text show the best fit parameters estimated using nls with LA VP calculated using vascular species only, red shows the pan-Arctic parameters used in Shaver ( , 2013, and blue shows best fit parameters with LA VP calculations that include moss. (B) Observed peakseason NDVI regressed against NDVI predicted from a multiple regression model using top cover (first pin hits only) or repeated cover of eight PFTs or (C) individual species as predictors. Goodness-of-fit statistics are inset. Regression coefficients are in Tables 1 and 2. https://doi.org/10.1371/journal.pone.0285030.g003

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slopes (a more rapid increase in ecosystem CO 2 uptake in response to increasing light). The LCP (where GPP = R eco ) was significantly predicted by cover of the five photosynthetic PFTs, but the fit was poor (F 5,62 = 2.7, r 2 = 0.18, P = 0.03). Nonetheless, deciduous shrub cover displayed a significant negative relationship with the LCP.

Model evaluation
Without moss included in LA VP calculations the LAI~NDVI Model (Eq (4)) did not predict observed LA VP well (r 2 = 0.012, P = 0.18, Fig 6A), overpredicting lower LA VP values and underpredicting higher values (predicted vs. observed slope = 0.38). With moss included in LA VP , the model performed better but the fit was still poor (r 2 = 0.23, P < 0.001, predicted vs. observed slope = 1.05) As a result, predictions from the Tundra GPP Model (Eq (5)) using LAI modeled from NDVI correlated with observed values (r 2 = 0.77, P < 0.001, Fig 6B), but did not follow a 1:1 relationship, tending to overestimate higher rates of GPP 600 and underestimate lower rates (predicted vs. observed slope = 1.94). When LA VP was used in the Tundra GPP Model, predicted values followed observed values very closely (r 2 = 0.76, predicted vs. observed slope = 0.94, P < 0.001).

Discussion
Our multi-scale investigation set out to answer two questions regarding NDVI, plant structure, and ecosystem function. First, does increasing NDVI in MAT indicate an increase in leaf area, deciduous shrub abundance, or both? And second, what are the implications of observed variation in NDVI for NEE, GPP, and ecosystem respiration (R eco )?
In answer to our first question, we found that NDVI is dominated by the biochemical characteristics of the leaves of the primary PFTs in MAT, more so than by cumulative leaf area (i.e. LA VP ). In the MAT plots, NDVI was more strongly driven by top cover and repeated cover of deciduous shrubs than any other co-dominant PFTs, and deciduous shrub monotypic plots

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exhibited much higher NDVI than other PFTs. Surprisingly, in our study plot-level vascular LA VP (LAI typically only accounts for vascular vegetation [9]) was a poor predictor of NDVI.
In answer to our second question, we found that vascular LA VP was a poor direct predictor of NEE, GPP, and R eco , whereas NDVI or PFT cover were significant predictors of all three fluxes. Together, these lines of evidence support the interpretation of recent increases in NDVI within MAT of the North American Arctic as indicative of increasing deciduous shrub abundance [16,18,42,43], with substantial implications for C uptake [44], forage quality [45], and ecosystem hydrology [46]. Our multiple regression results suggest that midsummer NDVI in MAT is effectively a twodimensional measurement of vegetation that is determined by the spectral characteristics of leaves at the top of the canopy. Despite making contributions to plot-level LA VP that were statistically similar to those of graminoids (Fig 2), the regression coefficients demonstrate that deciduous shrubs dominate the NDVI signal in this ecosystem (Table 1), as has been noted in

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other tundra studies [22,47]. This is supported by the two dominant deciduous shrubs, B. nana and S. pulchra, exhibiting higher leaf-level NDVI than the dominant graminoid, E. vaginatum, primarily due to much lower reflectance in the red band (Fig 4B), which was consistent with the patterns we observed when comparing the pure canopies of the monotypic plots ( Fig  4A). Thus, areas exhibiting increasing NDVI values within MAT [29] likely indicate horizontally expanding deciduous shrub canopies [48]. Next, many tundra-focused studies have used NDVI to estimate leaf area index (LAI) [9,11,49]. Thus, we were surprised to find no relationship in our data between NDVI and whole plot LA VP (Fig 3), which was further reflected in the poor fit between LAI modeled from NDVI (Eq (4)) and LA VP (Fig 6). Including moss in the LA VP calculation changed the LAI~NDVI relationship to statistically significant as LA VP captured more photosynthetically active tissues, but the fit between observed LA VP and LAI predicted from NDVI was still poor. Although LA VP is a variant that underestimates LAI [47], this metric (based on a point-frame with vertical pins) has been found to be closely related to LAI and aboveground biomass in other non-forested ecosystems [33,50]. Further, use of LA VP in the Tundra GPP Model yielded excellent agreement with observed levels of GPP (Fig 6), further validating it as a non-destructive estimator of LAI in this system. One explanation for the poor fit between LAI and NDVI may lay in high variation in leaf angle [47,51] and biochemical characteristics [45,52,53] in tussock tundra communities; both affect the reflectance characteristics of leaves when viewed from above (as in a NDVI measurement). However, this does not explain the better fits between LAI and NDVI found in previous studies [9,12]. This raises the possibility that variation in LAI and variation in deciduous shrub abundance may have been confounded in

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previous studies [9][10][11][12]. Plots with high LAI and high NDVI values may have exhibited high NDVI values because of abundant deciduous shrubs rather than because of high LAI alone. The implication of this finding is that NDVI may be a better tool for examining spatiotemporal variation in deciduous shrub abundance in Arctic MAT systems than previously thought.
While NDVI was not closely correlated with LA VP , it was closely correlated with GPP and NEE (Fig 5). Meanwhile, there was no evidence of a correlation between LA VP and GPP or NEE, despite LAI being one of the most commonly used predictors of CO 2 exchange in Arctic tundra [10,11]. Again, including moss in the LA VP calculations changed these model fits to

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statistically significant, but the relationship was still relatively coarse. Some of this may be due to our measurements of plot-level photosynthesis occurring during peak light conditions (a six-hour window straddling solar noon); the more vertically oriented leaves of graminoids [47,51] (which contribute substantially to LAI in this system (Fig 2B)) may demonstrate greater photosynthesis (relative to shrubs) with the sun at a lower angle. On the other hand, using a six-hour window and two months of the growing season we may have captured enough variation in solar angle to accurately represent the contributions of graminoids. In this case, results from our regression that used PFT cover to predict CO 2 exchange ( that greater in situ photosynthesis by deciduous shrub canopies leads to the strong correlation between NDVI and CO 2 exchange. Of the dominant PFT's, deciduous shrubs had the only significant relationship with both NEE and GPP. Analysis of the light curves suggest this is partly due to the higher quantum yield of deciduous shrub-dominated plots (Table 4) as well as the negative correlation between deciduous shrub cover and the plot-level LCP; as a result, deciduous shrub-dominated plots will achieve net uptake at lower light levels than plots dominated by other PFTs. Recent work with MAT species corroborates these patterns; S. pulchra in particular tends to demonstrate higher light-saturated photosynthesis than E. vaginatum during mid to late summer [52,54], while B. nana has been found in some cases to exhibit greater photosynthesis throughout the season than E. vaginatum [55].
These findings are important in two ways. First, as vegetation in the Arctic change continues to change [26], these differences in leaf-level physiology between deciduous shrubs and graminoids in MAT will have substantial consequences for ecosystem C budgets. Second, studies that intend to model CO 2 exchange in the Arctic using remotely sensed spectral indices may benefit from considering NDVI as an indirect predictor of CO 2 exchange through its relationship with deciduous shrub abundance (and thus canopy photosynthetic capacity), with LAI as an independently estimated and potentially complimentary predictor. In our dataset, using NDVI to estimate LAI and LAI, in turn, to estimate CO 2 exchange [9][10][11][12] yielded GPP estimates that were substantially out of step with observed values (Fig 6B).
Amplified warming and changing spectral signatures of Arctic landscapes highlight the need for ground truthing efforts that link spectral reflectance with ecosystem structure and function. Our dataset, collected in one of the most spatially extensive vegetation communities in the Arctic, is consistent with an interpretation of Arctic greening as a phenomenon of deciduous shrubs expanding both horizontally and vertically, overtopping graminoids and thereby increasing NDVI [18,26] and C uptake [49]. Moreover, it appears that reflectance values at the plot scale can be approximated as linear combinations of the product of PFT top cover values, with shrub (and forb) cover the strongest drivers of the NDVI signal. On the other hand, NDVI displays little relationship with LA VP in moist acidic tundra, as leaf biochemical traits may override leaf quantity in both spectral and functional significance.